TAILIEUCHUNG - Báo cáo khoa học: "Speech emotion recognition with TGI"

We have adapted a classification approach coming from optical character recognition research to the task of speech emotion recognition. The classification approach enjoys the representational power of a syntactic method and efficiency of statistical classification. The syntactic part implements a tree grammar inference algorithm. We have extended this part of the algorithm with various edit costs to penalise more important features with higher edit costs for being outside the interval, which tree automata learned at the inference stage. The statistical part implements an entropy based decision tree (). We did the testing on the Berlin database of emotional speech | Speech emotion recognition with TGI .2 classifier Julia Sidorova Universitat Pompeu Fabra Barcelona Spain Abstract We have adapted a classification approach coming from optical character recognition research to the task of speech emotion recognition. The classification approach enjoys the representational power of a syntactic method and efficiency of statistical classification. The syntactic part implements a tree grammar inference algorithm. We have extended this part of the algorithm with various edit costs to penalise more important features with higher edit costs for being outside the interval which tree automata learned at the inference stage. The statistical part implements an entropy based decision tree . We did the testing on the Berlin database of emotional speech. Our classifier outperforms the state of the art classifier Multilayer Perceptron by and a baseline by which proves validity of the approach. 1 Introduction In a number of applications such as humancomputer interfaces smart call centres etc. it is important to be able to recognise people s emotional state. An aim of a speech emotion recognition SER engine is to produce an estimate of the emotional state of the speaker given a speech fragment as an input. The standard way to do SER is through a supervised machine learning procedure Sidorova et al. 2008 . It also should be noted that a number of alternative classification strategies has been offered recently such as unsupervised learning Liu et al. 2007 and numeric regression Grimm et al. 2007 etc and which are preferable under certain conditions. Our contribution is a new algorithm of a mixed design with syntactic and statistical learning which we borrowed from optical character recognition Sempere Lopez 2003 extended and adapted for SER. The syntactic part implements tree grammar inference Sakakibara 1997 and the statistical part implements Quinlan 1993 . The intuitive reasons underlying this .

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